Search papers, labs, and topics across Lattice.
GeoMind is introduced, a tool-augmented agentic framework for lithology classification that models the task as a sequential reasoning process. It uses perception, reasoning, and analysis modules coordinated by a global planner to infer lithology hypotheses from well logs and verify predictions against stratigraphic constraints. By optimizing intermediate reasoning steps through fine-grained process supervision, GeoMind achieves superior classification performance and transparent decision-making compared to existing static methods on four benchmark datasets.
Lithology classification gets a reasoning upgrade: GeoMind's agentic workflow beats static methods by grounding decisions in geological evidence and constraints.
Lithology classification in well logs is a fundamental geoscience data mining task that aims to infer rock types from multi dimensional geophysical sequences. Despite recent progress, existing approaches typically formulate the problem as a static, single-step discriminative mapping. This static paradigm limits evidence-based diagnostic reasoning against geological standards, often yielding predictions that are detached from geological reality due to a lack of domain priors. In this work, we propose GeoMind, a tool-augmented agentic framework that models lithology classification as a sequential reasoning process. GeoMind organizes its toolkit into perception, reasoning, and analysis modules, which respectively translate raw logs into semantic trends, infer lithology hypotheses from multi-source evidence, and verify predictions against stratigraphic constraints. A global planner adaptively coordinates these modules based on input characteristics, enabling geologically plausible and evidence-grounded decisions. To guarantee the logical consistency of GeoMind, we introduce a fine-grained process supervision strategy. Unlike standard methods that focus solely on final outcomes, our approach optimizes intermediate reasoning steps, ensuring the validity of decision trajectories and alignment to geological constraints. Experiments on four benchmark well-log datasets demonstrate that GeoMind consistently outperforms strong baselines in classification performance while providing transparent and traceable decision-making processes.
